Many systems and processes in science, engineering, business, and other settings can be characterized by the fact that many different inter-related parameters contribute to the behavior of the system or process. It is often desirable to determine values or ranges of values for some or all of these parameters. This may be done, for example, so that parameter values or value ranges corresponding to beneficial behavior patterns of the system or process (such as productivity, profitability, or efficiency) can be identified. However, the complexity of most real world systems generally precludes the possibility of arriving at such solutions analytically.
Many analysts have therefore turned to predictive models to characterize and derive solutions for these complex systems and processes. A predictive model is generally a representation of a system or process that receives input data or parameters (such as those related to a system or model attribute and/or external circumstances or environments) and generates output indicative of the behavior of the system or process under those parameters. In other words, the model or models may be used to predict the behavior or trends of the system or process based upon previously acquired data.